The paddy field is a major source of gaseous carbon and nitrogen emissions, and reducing these emissions is of great significance for mitigating greenhouse effects and non-point source pollution in farmland. Biochar, derived from agricultural waste, possesses a stable structure, large specific surface area, abundant pore structures, and surface functional groups. These characteristics could enhance soil physicochemical properties and microbial activity, thereby facilitating the dual goals of increasing crop yield and reducing emissions. Based on numerous studies, this review summarizes the effects of biochar on the emissions of carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O), and ammonia volatilization (NH₃), as well as on global warming potential (GWP) and greenhouse gas emission intensity (GHGI). It elucidates the mechanism of emission reduction by biochar amendment from the perspective of carbon and nitrogen conversion processes and soil physicochemical and biological properties. Numerous studies showed the application of 5~40 t ha⁻¹ biochar reduced CO₂, CH₄, N₂O, and NH₃ emissions by 1.64~89.6%, 8.6~89.6%, 10~90%, and 12.27~53%, respectively. A small number of studies found that the application of 5~48 t ha⁻¹ biochar increased CO₂, CH₄, N₂O, and NH₃ emissions by 12~37%, 19.85~176%, 13~84.23%, and 5.47~70.9%, respectively. Most scholars have found that biochar has varying degrees of emission reduction capabilities in different parts of the world. Therefore, future research directions have been suggested for utilizing biochar to reduce carbon and nitrogen emissions in paddy fields.

Groundwater serves as an indispensable global resource, essential for agriculture, industry, and the urban water supply. Predicting the groundwater level in karst regions presents notable challenges due to the intricate geological structures and fluctuating climatic conditions. This study examines Qingzhen City, China, introducing an innovative hybrid model, the Hodrick–Prescott (HP) filter–Long Short-Term Memory (LSTM) network (HP-LSTM), which integrates the HP filter with the LSTM network to enhance the precision of groundwater level forecasting. By attenuating short-term noise, the HP-LSTM model improves the long-term trend prediction accuracy. Findings reveal that the HP-LSTM model significantly outperformed the conventional LSTM, attaining R² values of 0.99, 0.96, and 0.98 on the training, validation, and test datasets, respectively, in contrast to LSTM values of 0.92, 0.76, and 0.95. The HP-LSTM model achieved an RMSE of 0.0276 and a MAPE of 2.92% on the test set, significantly outperforming the LSTM model (RMSE: 0.1149; MAPE: 9.14%) in capturing long-term patterns and reducing short-term fluctuations. While the LSTM model is effective at modeling short-term dynamics, it is more prone to noise, resulting in greater prediction errors. Overall, the HP-LSTM model demonstrates superior robustness for long-term groundwater level prediction, whereas the LSTM model may be better suited for scenarios requiring rapid adaptation to short-term variations. Selecting an appropriate model tailored to specific predictive needs can thus optimize groundwater management strategies.

NBC represents a novel application that significantly improves adsorption capacity for NH₄⁺–N, thereby reducing NH₃ volatilization by improving soil parameters.

Biochar (BC) demonstrates considerable potential for reducing nitrogen emissions. However, it frequently exhibits a limited capacity for the adsorption of NO₃⁻-N, thereby reducing its effectiveness in mitigating N₂O emissions. Nano-biochar (NBC) is attracting attention due to its higher surface energy, but there is a lack of information on enhancing NO₃⁻-N adsorption and reducing N₂O emissions. Accordingly, this study conducted batch adsorption experiments for NO₃⁻-N and simulated N₂O emissions experiments. The NO₃⁻-N adsorption experiments included two treatments: bulk BC and NBC; the N₂O emissions experiments involved three treatments: a no-biochar control, BC, and NBC. N₂O emissions experiments were incorporated into the soil at mass ratios of 0.3%, 0.6%, 1%, and 3%. The results demonstrate that NBC exhibits nearly twice the NO₃⁻-N adsorption capacity compared to bulk biochar (BC), with adsorption behavior best described by a physical adsorption model. The enhanced adsorption performance was primarily attributed to NBC’s significantly increased specific surface area, pore volume, abundance of surface acidic functional groups, and higher aromaticity, which collectively strengthened multiple sorption mechanisms, including physical adsorption, electrostatic interactions, π–π interactions, and apparent ion exchange. In addition, NBC application (0.3–3%) reduced cumulative N₂O emissions by 11.60–54.77%, outperforming BC (9.16–32.65%). NBC treatments also increased soil NH₄⁺-N and NO₃⁻-N concentrations by 2.4–8.2% and 7.3–59.0%, respectively, indicating improved inorganic N retention. Overall, NBC demonstrated superior efficacy over bulk BC in mitigating N₂O emissions and conserving soil nitrogen, highlighting its promise as a sustainable amendment for integrated nutrient management and greenhouse gas reduction in soil.

Background: Zeolite (Z) is gradually used in rice production due to its holding ability for water and nutrients, but limited information is available on how its physiological function affects rice grain yield and quality under water stress. Methods: This study aimed to investigate the effect of Z application on rice physiological characteristics, dry matter and nitrogen accumulation, grain yield and quality under continuous flooding (CF) and alternate wetting and drying irrigation (AWD). Results: The results showed that, compared with CF, AWD reduced leaf SPAD, root bleeding intensity, aboveground dry matter and nitrogen accumulation, resulted in lower grain yield without Z application, but improved root–shoot ratio and root N accumulation. Z application increased dry matter and N accumulation, and subsequent grain yield by improving leaf SPAD and root bleeding intensity. Both AWD and Z application improved water use efficiency. AWD reduced head rice rate, chalky rice rate and chalkiness, but improved the taste value by increasing the breakdown and reducing the setback. Z application improved protein content, reduced breakdown and setback, but increased chalky rice rate and chalkiness. Conclusions: These results indicated that AWD and Z application could achieve several benefits including improved grain yield, grain quality and water use efficiency.